• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.389-395
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• 2013

Ethernet is the most widely deployed access network protocol around the world. IEEE 802.3az WG released the EEE standard based on LPI mode to improve the energy efficiency of Ethernet. This paper proposes improved adaptive power control mechanism that can enhance energy-efficiency based on EEE from small Ethernet switch. The feature of this mechanism is that it predicts the traffic characteristic of next cycle by measuring the amount of traffic flowing in during certain period and adjusts the optimal threshold value to relevant traffic load. Performance evaluation results indicate that the proposed mechanism improves overall performance compared to traditional mechanism, since it significantly reduces energy consumption rate, even though average packet delay increases a little bit.

• Journal of the Society of Naval Architects of Korea
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• v.39 no.1
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• pp.1-7
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• 2002

As the existing noise measuring device was affected the flow-field and structural vibration directly, new experimental device was required. Two Hydrophone Boxes are designed and their performances are investigated. The noise level of the KRISO cavitation tunnel is compared with those of the other cavitation tunnels which have been designed for the noise study. The present experimental results show the possibility of the full-scale prediction for propeller cavitation noise and the improvement of the measurement performance at the range of low-frequency.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.79-84
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• 2013

In this paper, the method of multiple PS(pilot symbol) QAM channel compensation is suggested in order to analyze and improve occurring problems in case of delay waves in Frequency-Selective Rayleigh fading channels through Pilot Symbol Assisted Modulation(PSAM) which is a method predicting and compensating fading information, using Pilot Symbol in flat fading channels. This suggested method shows stable improvement in its performance even though it is effected by the level of delay on delay waves while the existing PSAM method has severe malfunction with a small amount of level of delay on delay waves regardless of signal-to-noise ratio(SNR).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12C
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• pp.1149-1155
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• 2007

In this paper, we propose a new overlap and add speech synthesis method which demonstrates improved continuity performance. The proposed method uses a weighted phase error function and minimizes the wave discontinuity of the synthesis signal, rather than the phase discontinuity, to estimate the mid-point phase. Experimental results show that the proposed method improves the continuity between the synthesized signals relative to the existing method.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.512-519
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• 2003

In this paper, we propose a neuro-fuzzy modeling to improve the performance using the hierarchical clustering and Gaussian Mixture Model(GMM). The hierarchical clustering algorithm has a property of producing unique parameters for the given data because it does not use the object function to perform the clustering. After optimizing the obtained parameters using the GMM, we apply them as initial parameters for Adaptive Network-based Fuzzy Inference System. Here, the number of fuzzy rules becomes to the cluster numbers. From this, we can improve the performance index and reduce the number of rules simultaneously. The proposed method is verified by applying to a neuro-fuzzy modeling for Box-Jenkins s gas furnace data and Sugeno's nonlinear system, which yields better results than previous oiles.

• The Journal of the Korea Contents Association
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• v.18 no.8
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• pp.525-535
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• 2018

If the frequency of a particular class is excessively higher than the frequency of other classes in the classification problem, data imbalance problems occur, which make machine learning distorted. Corporate bankruptcy prediction often suffers from data imbalance problems since the ratio of insolvent companies is generally very low, whereas the ratio of solvent companies is very high. To mitigate these problems, it is required to apply a proper sampling technique. Until now, oversampling techniques which adjust the class distribution of a data set by sampling minor class with replacement have popularly been used. However, they are a risk of overfitting. Under this background, this study proposes ROSE(Random Over Sampling Examples) technique which is proposed by Menardi and Torelli in 2014 for the effective corporate bankruptcy prediction. The ROSE technique creates new learning samples by synthesizing the samples for learning, so it leads to better prediction accuracy of the classifiers while avoiding the risk of overfitting. Specifically, our study proposes to combine the ROSE method with SVM(support vector machine), which is known as the best binary classifier. We applied the proposed method to a real-world bankruptcy prediction case of a Korean major bank, and compared its performance with other sampling techniques. Experimental results showed that ROSE contributed to the improvement of the prediction accuracy of SVM in bankruptcy prediction compared to other techniques, with statistical significance. These results shed a light on the fact that ROSE can be a good alternative for resolving data imbalance problems of the prediction problems in social science area other than bankruptcy prediction.

• Convergence Security Journal
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• v.7 no.2
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• pp.17-25
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• 2007

In this paper, an algorithm that provisions proportional differentiation of packet delays is proposed with an objective for enhancing quality of service (QoS) in future packet networks. It features an adaptive scheme that adjusts the target delay every time slot to compensate the deviation from the target delay which is caused by the prediction error on the traffic to be arrived in the next time slot. It predicts the traffic to be arrived at the beginning of a time slot and measures the actual arrived traffic at the end of the time slot. The difference between them is utilized to the delay control operation for the next time slot to offset it. As it compensates the prediction error continuously, it shows superior adaptability to the bursty traffic as well as the exponential rate traffic. It is demonstrated through simulations that the algorithm meets the quantitative delay bounds and shows superiority to the traffic fluctuation in comparison with the conventional non-adaptive mechanism. The algorithm is implemented with VHDL on a Xilinx Spartan XC3S1500 FPGA and the performance is verified under the test board based on the XPC860P CPU.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.510-518
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• 2023

This paper introduces a MATLAB graphical user interface (GUI) based software for performance analysis of navigation message and wide area differential correction of regional navigation satellite system (RNSS). This software was developed to analyze satellite orbit/clock-related performance of navigation message and wide area differential correction simulating RNSS for regions near Korea based on different distributions of monitor and reference stations. As a result of software operation, navigation message and wide area differential correction are given as output in MATLAB file format. From the analysis of output, it was confirmed that valid navigation message and wide area differential correction could be generated from the results about statistical feature of orbit and clock prediction errors, cm-level fitting errors for navigation message parameters, and 81.9% enhancement in range error for wide area differential correction.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.5
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• pp.526-534
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• 2014

In this study, single-input transfer function model is applied to forecast monthly mean sea surface temperature(SST) in 2010 at Yeosu in Korean coastal waters. As input series, monthly mean air temperature series for ten years(2000-2009) at Yeosu in Korea is used, and Monthly mean SST at Yeosu station in Korean coastal waters is used as output series(the same period of input). To build transfer function model, first, input time series is prewhitened, and then cross-correlation functions between prewhitened input and output series are determined. The cross-correlation functions have just two significant values at time lag at 0 and 1. The lag between input and output series, the order of denominator and the order of numerator of transfer function, (b, r, s) are identified as (0, 1, 0). The selected transfer function model shows that there does not exist the lag between monthly mean air temperature and monthly mean SST, and that transfer function has a first-order autoregressive component for monthly mean SST, and that noise model was identified as $ARIMA(1,0,1)(2,0,0)_{12}$. The forecasted values by the selected transfer function model are generally $0.3-1.3^{\circ}C$ higher than actual SST in 2010 and have 6.4 % mean absolute percentage error(MAPE). The error is 2 % lower than MAPE by ARIMA model. This implies that transfer function model could be more available than ARIMA model in terms of forecasting performance of SST.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.245-253
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• 2021

Purpose: This study develops a risk prediction model that predicts the risk of a fire site by using initial information such as building information and reporter acquisition information, and supports effective mobilization of fire fighting resources and the establishment of damage minimization strategies for appropriate responses in the early stages of a disaster. Method: In order to identify the variables related to the fire damage scale on the fire statistics data, a correlation analysis between variables was performed using a machine learning algorithm to examine predictability, and a learning data set was constructed through preprocessing such as data standardization and discretization. Using this, we tested a plurality of machine learning algorithms, which are evaluated as having high prediction accuracy, and developed a risk prediction model applying the algorithm with the highest accuracy. Result: As a result of the machine learning algorithm performance test, the accuracy of the random forest algorithm was the highest, and it was confirmed that the accuracy of the intermediate value was relatively high for the risk class. Conclusion: The accuracy of the prediction model was limited due to the bias of the damage scale data in the fire statistics, and data refinement by matching data and supplementing the missing values was necessary to improve the predictive model performance.